Field of the Invention
This invention relates to digital maps of the type for displaying road or pathway information, and more specifically toward a method for supplementing a digital map with longitudinal speed profile information and toward a navigation device or any GNSS enabled unit used in conjunction therewith.
Related Art
Personal navigation devices like that shown generally at 10 in FIG. 1, for example, utilize digital maps combined with accurate positioning data from GPS or other data streams. These devices 10 have been developed for commuters seeking navigation assistance, for businesses trying to minimize transportation costs, and many other useful applications. The effectiveness of such navigation systems is inherently dependent upon the accuracy and completeness of the information provided to it in the forms of digital maps and associated features and attribute data. Likewise, the effectiveness of such navigation systems is also dependent upon accurately and quickly matching the actual, real-world location of the navigation device to a corresponding portion of the digital map. Typically, a navigation system 10 includes a display screen 12 or graphic user interface that portrays a network of streets as a series of line segments, including a center line running approximately along the center of each street or path, as exemplified in FIG. 1. The traveler can then be generally located on the digital map close to or with regard to that center line. Such GPS-enabled personal navigation devices, such as those manufactured by TomTom N.V. (www.tomtom.com), may be also configured as probes to record its position at regular intervals. Such probe data points comprise a sequence of discrete positions recorded at a particular time of the day taken at intervals of, for example, one second. Of course, other suitable devices may be used to generate probe data points including handheld devices, mobile phones, PDAs, and the like.
It is known, for example, to take probe data inputs from low-cost positioning systems, handheld devices and GPS-enabled mobile phones. The probe data, which creates the nodes or probe positions at regular intervals, can be transmitted to a collection service or other map making or data analysis service via wireless transmission, communicated via Internet uploads, or otherwise collected for analysis. Through this technique road geometry, features and attributes can be inferred, and traffic flow patterns and behaviors discerned. FIG. 2 is a representative example of raw probe data reflecting positions collected over a period of days from a downtown, city-center area of Ottawa, Canada. From this raw probe data, even an untrained eye can begin to discern road geometries. Each data point represented in the illustration of FIG. 2 includes information as to the particular time of day that the data point was recorded. Thus, while FIG. 2 depicts only position data, the recorded data also provides a time stamp for each position. Furthermore, each individual probe creates a trace which can be analyzed for travel speeds, accelerations, stops, and the like.
Traditional routing methods use maximum speed limits as exist along road segments to calculate travel time estimates, however in practice speed limit information is not accurate because these speeds are not always possible at various times of the day. Speed profiles have been derived by intensively processing this probe data to create average traffic speeds for each road segment, i.e., for each section of road in the digital map, for different time slots or times of the day. See, for example, the TomTom IQ Routes™ product. See for example FIG. 4 which shows several derived speed profiles for a particular road segment (AB) during several different time spans of 30-minute increments.
The IQ Routes™ product uses anonymous probe data to discover actual patterns in driving speeds. Typically, route calculations before IQ Routes used 0.85% of the maximum speed limit in its calculation—IQ Routes by contrast uses the speeds actually driven on those roads. (Alternatively, a likely speed value can be derived from the road classification. E.g. when legal speed limits are not available.) This data is applied to a profile model and patterns in the road speeds are identified in time spans (e.g., 5 minute increments) throughout the day. The speed profiles are applied to the road segments, building up an accurate picture of speeds using historical data. All of these speed profiles are added to the existing IQ Routes data built into the map stored in the navigation device 10, to make it even more accurate and useful for premium routing and travel time estimates. Speed profiles therefore represent a continuous or semi-continuous averaged speed distribution of vehicles derived from probe information, driving along the same section of the road and direction. Speed profiles reflect speed variations per segment per time interval, but are not longitudinally distributed in the sense that they do not describe velocity variations along the length of a link or road segment.
While very useful, these prior art speed profile techniques do not provide any indication of the most efficient speed at which to drive any particular road segment, or indeed any indication of the actual manner (e.g., acceleration/deceleration rates) in which a vehicle traverses a particular road segment. It is known, for example, that vehicles driven with frequent start-stop type motions and aggressive accelerations-decelerations are very energy inefficient. Conversely, maintaining a vehicle at a steady speed, particularly if the speed is around the commonly accepted optimum vehicle speed of about 56 mph, and moderating accelerations-decelerations is a far more energy efficient strategy. In the real world, one road segment will vary dramatically from the next road segment in terms of the particular bends, lane consolidations or lane expansions, traffic controls, and other measures that affect traffic speed in addition to the instantaneous traffic volume. For these reasons, it is often impossible to achieve optimum vehicle efficiency by driving a vehicle at a constant speed.
Because prior art techniques do not indicate the most efficient speeds and acceleration-deceleration rates at which to drive any particular road segment, there has been a lack of useful information pertaining to real-time traffic flow conditions as may exist along any particular road segment or section of roads.
It is therefore desirable to ascertain the most efficient manner, e.g., speeds and possibly acceleration-deceleration rates, in which to drive any particular road segment. With such information, it would be possible to provide real-time energy efficient driving instructions for drivers operating with a position determining and/or navigation-capable device, which information accounts for the unique characteristics of any particular road segment. Furthermore, knowledge of the most efficient manner in which to drive a particular road segment will enable accurate, real-time assessments of the traffic flow conditions along that road segment.